Journals with open-discussion forums are excellent educational resources for peer review training exercises
Bibliographic record
Abstract
Abstract. Journals with open-discussion forums lend themselves well for peer review exercises to train early career scientists. Earth System Science Data (ESSD) is an open-access journal for the publication of interdisciplinary datasets and articles, and it is thus an example of an educational resource in the peer review process. We offer our experiences in peer review training with manuscripts submitted to ESSD, and we do so from the disparate perspectives of workshop instructor, student, and author. We then provide recommendations for the structure of a peer review workshop. We seek to promote the use of open-discussion forums, including ESSD, for educational purposes, as they can provide mutual benefits to trainees, authors, reviewers, and editors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.096 | 0.360 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.232 | 0.173 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".